def test_band_snr_norm(testing_spectrum, sampling):
"""Compared to wav snr norm."""
wav, flux = testing_spectrum
wav, flux = convolve_and_resample(
wav, flux, vsini=1, R=100000, band="J", sampling=sampling
)
assert snrnorm.snr_constant_band(
wav, flux, band="J", snr=100, sampling=sampling
) == snrnorm.snr_constant_wav(wav, flux, wav_ref=1.25, snr=100, sampling=sampling)
assert snrnorm.snr_constant_band(
wav, flux, band="J", snr=100, sampling=sampling
) != snrnorm.snr_constant_wav(wav, flux, wav_ref=1.24, snr=100, sampling=sampling)
def test_snr_normalization(desired_snr, band, testing_spectrum):
"""Test SNR after normalizing function is the desired value.
Testing on middle of J band.
"""
wav, flux = testing_spectrum
band_mid = utils.band_middle(band)
# Searching for the closest index to 1.25
index_reference = np.searchsorted(wav, [band_mid])[0]
snr_estimate = np.sqrt(
np.sum(flux[index_reference - 1:index_reference + 2]))
assert round(snr_estimate, 1) != desired_snr # Assert SNR is not correct
norm_const = snrnorm.snr_constant_band(wav,
flux,
snr=desired_snr,
band=band)
new_flux = flux / norm_const
new_snr_estimate = np.sqrt(
np.sum(new_flux[index_reference - 1:index_reference + 2]))
assert round(new_snr_estimate, 0) == desired_snr
def real_spec(request):
band = request.param
wav, flux = load_aces_spectrum([3900, 4.5, 0.0, 0])
wav, flux = wav_selector(wav, flux, *band_limits(band))
flux = flux / snr_constant_band(wav, flux, 100, band)
atm = Atmosphere.from_band(band).at(wav)
return wav, flux, atm.transmission
def test_snr_normalization_constant(desired_snr, band, testing_spectrum):
"""Test snr_constant_band and snr_constant_wav produce same result."""
wav, flux = testing_spectrum
band_mid = utils.band_middle(band)
assert snrnorm.snr_constant_band(
wav, flux, band=band, snr=desired_snr
) == snrnorm.snr_constant_wav(wav, flux, band_mid, snr=desired_snr)
def test_band_snr_norm_test_data():
"""Compared to wav snr norm."""
# snr_constant_band
star, band, vel, res = "M0", "J", 1.0, "100k"
test_data = os.path.join(eniric.paths["resampled"],
resampled_template.format(star, band, vel, res))
wav, flux = io.pdread_2col(test_data)
assert snrnorm.snr_constant_band(
wav, flux, band="J", snr=100) == snrnorm.snr_constant_wav(wav,
flux,
wav_ref=1.25,
snr=100)
assert snrnorm.snr_constant_band(wav, flux, band="J",
snr=100) != snrnorm.snr_constant_wav(
wav, flux, wav_ref=1.24, snr=100)
def test_snr_constant_band_returns_mid_value_const(band):
size = 100
np.random.seed(40)
flux = 500 * np.random.rand(
size
) # To give a random spectrum (but consistent between tests)
lim = utils.band_limits(band)
wav = np.linspace(lim[0], lim[1], size)
band_const = snrnorm.snr_constant_band(wav, flux, band=band)
wav_const = snrnorm.snr_constant_wav(wav, flux, wav_ref=utils.band_middle(band))
assert isinstance(band_const, float)
assert isinstance(wav_const, float)
assert band_const == wav_const # Since band calls wave at midpoint
def normalize_flux(
flux: ndarray,
id_string: str,
new: bool = True,
snr: Union[int, float] = 100.0,
ref_band: str = "J",
sampling: Union[int, float] = 3.0,
) -> ndarray:
"""Normalize flux to have SNR of 100 in middle of reference band.
Parameters
----------
flux: ndarray
Photon flux.
id_string: str
Identifying string for spectra.
new: bool default=True
Choose between new and old constant for testing.
snr: int, float default=100
SNR to normalize to, .
ref_band: str default="J"
References band to normalize to.
sampling: int or float
Number of pixels per resolution element.
Returns
-------
normalized_flux: ndarray
Flux normalized to a S/N of SNR in the middle of the ref_band.
"""
# print("Starting norm of {}".format(id_string))
if new:
if ref_band.upper() == "SELF":
__, ref_band, __, __ = decompose_id_string(id_string)
wav_ref, flux_ref = get_reference_spectrum(id_string, ref_band)
norm_const = snr_constant_band(
wav_ref, flux_ref, snr=snr, band=ref_band, sampling=sampling
)
else:
if (ref_band.upper() != "J") or (snr != 100):
raise ValueError("The old way does not work with these reference values.")
norm_const = old_norm_constant(id_string) * 1e4 # Input flux offset
print("{0:s} normalization constant = {1:f}".format(id_string, norm_const))
return flux / norm_const
def test_snr_normalization_logic(band):
"""Testing direct value.
snr = sqrt(sum(3 pixels))
const = (snr/ref_snr)**2
if pixel value = 3 then the normalization constant will be 1.
"""
size = 100
band = "K"
lim = utils.band_limits(band)
wav = np.linspace(lim[0], lim[1], size)
flux = 3 * np.ones(size)
band_const = snrnorm.snr_constant_band(wav, flux, snr=3, band=band)
wav_const = snrnorm.snr_constant_wav(
wav, flux, snr=3, wav_ref=(lim[0] + lim[1]) / 2
)
assert band_const == 1
assert wav_const == 1
def test_snr_constant_band_with_invalid_wavelength(wav, band):
with pytest.raises(ValueError):
snrnorm.snr_constant_band(wav, np.ones(50), band=band)
def do_analysis(
star_params,
vsini: float,
R: float,
band: str,
sampling: float = 3.0,
conv_kwargs=None,
snr: float = 100.0,
ref_band: str = "J",
rv: float = 0.0,
air: bool = False,
model: str = "aces",
verbose: bool = False,
) -> Tuple[Quantity, ...]:
"""Calculate RV precision and Quality for specific parameter set.
Parameters
----------
star_param:
Stellar parameters [temp, logg, feh, alpha] for phoenix model libraries.
vsini: float
Stellar equatorial rotation.
R: float
Instrumental resolution.
band: str
Spectral band.
sampling: float (default=False)
Per pixel sampling (after convolutions)
conv_kwargs: Dict (default=None)
Arguments specific for the convolutions,
'epsilon', 'fwhm_lim', 'num_procs', 'normalize', 'verbose'.
snr: float (default=100)
SNR normalization level. SNR per pixel and the center of the ref_band.
ref_band: str (default="J")
Reference band for SNR normalization.
rv: float
Radial velocity in km/s (default = 0.0).
air: bool
Get model in air wavelengths (default=False).
model: str
Name of synthetic library (aces, btsettl) to use. Default = 'aces'.
verbose:
Enable verbose (default=False).
Returns
-------
q: astropy.Quality
Spectral quality.
result_1: astropy.Quality
RV precision under condition 1.
result_2 : astropy.Quality
RV precision under condition 2.
result_3: astropy.Quality
RV precision under condition 3.
Notes
-----
We apply the radial velocity doppler shift after
- convolution (rotation and resolution)
- resampling
- SNR normalization.
in this way the RV only effects the precision due to the telluric mask interaction.
Physically the RV should be applied between the rotational and instrumental convolution
but we assume this effect is negligible.
"""
if conv_kwargs is None:
conv_kwargs = {
"epsilon": 0.6,
"fwhm_lim": 5.0,
"num_procs": num_cpu_minus_1,
"normalize": True,
"verbose": verbose,
}
if ref_band.upper() == "SELF":
ref_band = band
model = check_model(model)
if model == "aces":
wav, flux = load_aces_spectrum(star_params, photons=True, air=air)
elif model == "btsettl":
wav, flux = load_btsettl_spectrum(star_params, photons=True, air=air)
else:
raise Exception("Invalid model name reached.")
wav_grid, sampled_flux = convolve_and_resample(wav, flux, vsini, R, band,
sampling, **conv_kwargs)
# Doppler shift
try:
if rv != 0:
sampled_flux = doppler_shift_flux(wav_grid, sampled_flux, vel=rv)
except Exception as e:
print("Doppler shift was unsuccessful")
raise e
# Scale normalization for precision
wav_ref, sampled_ref = convolve_and_resample(wav, flux, vsini, R, ref_band,
sampling, **conv_kwargs)
snr_normalize = snr_constant_band(wav_ref,
sampled_ref,
snr=snr,
band=ref_band,
sampling=sampling,
verbose=verbose)
sampled_flux = sampled_flux / snr_normalize
if (ref_band == band) and verbose:
mid_point = band_middle(ref_band)
index_ref = np.searchsorted(
wav_grid,
mid_point) # searching for the index closer to 1.25 micron
snr_estimate = np.sqrt(
np.sum(sampled_flux[index_ref - 1:index_ref + 2]))
print(
"\tSanity Check: The S/N at {0:4.02} micron = {1:4.2f}, (should be {2:g})."
.format(mid_point, snr_estimate, snr))
# Load Atmosphere for this band.
atm = Atmosphere.from_band(band=band, bary=True).at(wav_grid)
assert np.allclose(atm.wl,
wav_grid), "The atmosphere does not cover the wav_grid"
# Spectral Quality/Precision
q = quality(wav_grid, sampled_flux)
# Precision given by the first condition:
result_1 = rv_precision(wav_grid, sampled_flux, mask=None)
# Precision as given by the second condition
result_2 = rv_precision(wav_grid, sampled_flux, mask=atm.mask)
# Precision as given by the third condition: M = T**2
result_3 = rv_precision(wav_grid, sampled_flux, mask=atm.transmission**2)
# Turn quality back into a Quantity (to give it a .value method)
q = q * u.dimensionless_unscaled
return q, result_1, result_2, result_3
def do_analysis(
star_params,
vsini: float,
R: float,
band: str,
sampling: float = 3.0,
conv_kwargs=None,
snr: float = 100.0,
ref_band: str = "J",
rv: float = 0.0,
air: bool = False,
model="phoenix",
):
"""Precision and Quality for specific parameter set.
Parameters
----------
air: bool
Get model in air wavelengths.
model: str
Name of synthetic library to use. (phoenix, btsettl).
rv: float
Radial velocity.
Notes:
We apply the radial velocity doppler shift after
- convolution (rotation and resolution)
- resampling
- SNR normalization.
in this way the RV only effects the precision due to the telluric mask interaction.
The RV should maybe come between the rotational and instrumental convolution
but we assume this effect is negligible.
"""
if conv_kwargs is None:
conv_kwargs = {
"epsilon": 0.6,
"fwhm_lim": 5.0,
"num_procs": num_procs_minus_1,
"normalize": True,
}
if ref_band.upper() == "SELF":
ref_band = band
if model == "phoenix":
# Full photon count spectrum
wav, flux = load_aces_spectrum(star_params, photons=True, air=air)
elif model == "btsettl":
wav, flux = load_btsettl_spectrum(star_params, photons=True, air=air)
else:
raise ValueError(
"Model name error in '{}'. Valid choices are 'phoenix and 'btsettl'"
.format(model))
wav_grid, sampled_flux = convolve_and_resample(wav, flux, vsini, R, band,
sampling, **conv_kwargs)
# Doppler shift
try:
if rv != 0:
sampled_flux = doppler_shift_flux(wav_grid, sampled_flux, vel=rv)
except Exception as e:
print("Doppler shift was unsuccessful")
raise e
# Spectral Quality
q = quality(wav_grid, sampled_flux)
# Scale normalization for precision
wav_ref, sampled_ref = convolve_and_resample(wav, flux, vsini, R, ref_band,
sampling, **conv_kwargs)
snr_normalize = snr_constant_band(wav_ref,
sampled_ref,
snr=snr,
band=ref_band,
sampling=sampling)
sampled_flux = sampled_flux / snr_normalize
if ref_band == band:
mid_point = band_middle(ref_band)
index_ref = np.searchsorted(
wav_grid,
mid_point) # searching for the index closer to 1.25 micron
snr_estimate = np.sqrt(
np.sum(sampled_flux[index_ref - 1:index_ref + 2]))
print(
"\tSanity Check: The S/N at {0:4.02} micron = {1:4.2f}, (should be {2:g})."
.format(mid_point, snr_estimate, snr))
# Load Atmosphere for this band.
atm = Atmosphere.from_band(band=band, bary=True).at(wav_grid)
assert np.allclose(atm.wl,
wav_grid), "The atmosphere does not cover the wav_grid"
# Spectral Quality/Precision
q = quality(wav_grid, sampled_flux)
# Precision given by the first condition:
prec1 = rv_precision(wav_grid, sampled_flux, mask=None)
# Precision as given by the second condition
prec2 = rv_precision(wav_grid, sampled_flux, mask=atm.mask)
# Precision as given by the third condition: M = T**2
prec3 = rv_precision(wav_grid, sampled_flux, mask=atm.transmission**2)
# Turn quality back into a Quantity (to give it a .value method)
q = q * u.dimensionless_unscaled
return [q, prec1, prec2, prec3]